scholarly journals Assessing The Impact of A Restrictive Opioid Prescribing Law in West Virginia

2020 ◽  
Author(s):  
Cara L. Sedney ◽  
Maryam Khodaverdi ◽  
Robin Pollini ◽  
Patricia Dekeseredy ◽  
Nathan Wood ◽  
...  
Keyword(s):  
Time Series ◽  
West Virginia ◽  
Moving Average ◽  
Monitoring Program ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Emergency Rooms ◽  
Opioid Prescribing ◽  
The Impact ◽  
First Time

Abstract Background: The Opioid Reduction Act (SB 273) took effect in West Virginia in June 2018. This legislation limited ongoing chronic opioid prescriptions to 30 days’ supply, and first-time opioid prescriptions to 7 days’ supply for surgeons and 3 days’ for emergency rooms and dentists. The purpose of this study was to determine the effect of this legislation on reducing opioid prescriptions in West Virginia, with the goal of informing future similar policy efforts. Methods: Data were requested from the state Prescription Drug Monitoring Program (PDMP) including overall number of opioid prescriptions, number of first-time opioid prescriptions, average daily morphine milligram equivalents (MME) and prescription duration (expressed as “day’s supply”) given to adults during the 64 week time periods before and after legislation enactment. Statistical analysis was done utilizing an autoregressive integrated moving average (ARIMA) interrupted time series analysis to assess impact of both legislation announcement and enactment while controlling secular trends and considering autocorrelation trends. Benzodiazepine prescriptions were utilized as a control.Results: Our analysis demonstrates a statistically significant decrease in overall state opioid prescribing as well as average daily MME associated with the date of the legislation’s enactment when considering serial correlation in the time series and accounting for pre-intervention trends. There was no such association found with benzodiazepine prescriptions.Conclusion: Results of the current study suggest that SB 273 was associated with an average 22.1% decrease of overall opioid prescriptions and a small overall decrease of average daily MME relative to the date of legislative implementation in West Virginia. There was, however, no association of the legislation on first-time opioid prescriptions or days’ supply of opioid medication, and all variables were trending downward prior to implementation of SB 273. The control demonstrated no relationship to the law.

scholarly journals Assessing the impact of a restrictive opioid prescribing law in West Virginia

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Cara L. Sedney ◽  
Maryam Khodaverdi ◽  
Robin Pollini ◽  
Patricia Dekeseredy ◽  
Nathan Wood ◽  
...  
Keyword(s):  
Time Series ◽  
West Virginia ◽  
Moving Average ◽  
Monitoring Program ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Opioid Prescribing ◽  
Small Change ◽  
The Impact ◽  
First Time

Abstract Background The Opioid Reduction Act (SB 273) took effect in West Virginia in June 2018. This legislation limited ongoing chronic opioid prescriptions to 30 days’ supply, and first-time opioid prescriptions to 7 days’ supply for surgeons and 3 days’ for emergency rooms and dentists. The purpose of this study was to determine the effect of this legislation on reducing opioid prescriptions in West Virginia, with the goal of informing future similar policy efforts. Methods Data were requested from the state Prescription Drug Monitoring Program (PDMP) including overall number of opioid prescriptions, number of first-time opioid prescriptions, average daily morphine milligram equivalents (MME) and prescription duration (expressed as “days’ supply”) given to adults during the 64 week time periods before and after legislation enactment. Statistical analysis was done utilizing an autoregressive integrated moving average (ARIMA) interrupted time series analysis to assess impact of both legislation announcement and enactment while controlling secular trends and considering autocorrelation trends. Benzodiazepine prescriptions were utilized as a control. Results Our analysis demonstrates a significant decrease in overall state opioid prescribing as well as a small change in average daily MME associated with the date of the legislation’s enactment when considering serial correlation in the time series and accounting for pre-intervention trends. There was no such association found with benzodiazepine prescriptions. Conclusion Results of the current study suggest that SB 273 was associated with an average 22.1% decrease of overall opioid prescriptions and a small change in average daily MME relative to the date of legislative implementation in West Virginia. There was, however, no association of the legislation on first-time opioid prescriptions or days’ supply of opioid medication, and all variables were trending downward prior to implementation of SB 273. The control demonstrated no relationship to the law.

scholarly journals Assessment of the Impact of COVID-19 pandemic on population level interest in Skincare: Evidence from a google trends-based Infodemiology study

2020 ◽  
Author(s):  
Hasan Symum ◽  
Md. F. Islam ◽  
Habsa K. Hiya ◽  
Kh M. Ali Sagor
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Population Level ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Search Volume ◽  
Quasi Experimental ◽  
Relative Search Volume ◽  
The Impact

AbstractBackgroundCOVID-19 pandemic created an unprecedented disruption of daily life including the pattern of skin related treatments in healthcare settings by issuing stay-at-home orders and newly coronaphobia around the world.ObjectiveThis study aimed to evaluate whether there are any significant changes in population interest for skincare during the COVID-19 pandemic.MethodsFor the skincare, weekly RSV data were extracted for worldwide and 23 counties between August 1, 2016, and August 31, 2020. Interrupted time-series analysis was conducted as the quasi-experimental approach to evaluate the longitudinal effects of COVID-19 skincare related search queries. For each country, autoregressive integrated moving average (ARIMA) model relative search volume (RSV) time series and then testing multiple periods simultaneously to examine the magnitude of the interruption. Multivariate linear regression was used to estimate the correlation between countries’ relative changes in RSV with COVID-19 confirmed cases/ per 10000 patients and lockdown measures.ResultsOut of 23 included countries in our study, 17 showed significantly increased (p<0.01) RSVs during the lockdown period compared with the ARIMA forecasted data. The highest percentage of increments occurs in May and June 2020 in most countries. There was also a significant correlation between lockdown measures and the number of COVID-19 cases with relatives changes in population interests for skincare.ConclusionUnderstanding the trend and changes in skincare public interest during COVID-19 may assist health authorities to promote accessible educational information and preventive initiatives regarding skin problems.

scholarly journals Assessing the impact of SIGN 136 on opioid prescribing rates in Scotland: An interrupted time series analysis

2021 ◽  
Author(s):  
Harry L. Hébert ◽  
Daniel R. Morales ◽  
Nicola Torrance ◽  
Blair H. Smith ◽  
Lesley A. Colvin
Keyword(s):  
Chronic Pain ◽  
Time Series ◽  
Time Series Analysis ◽  
Interrupted Time Series ◽  
Relative Reduction ◽  
Interrupted Time Series Analysis ◽  
Post Intervention ◽  
Opioid Prescribing ◽  
Prescribing Trends ◽  
The Impact

AbstractBackgroundOpioids are used to treat patients with chronic pain, but their long-term use is associated with harms. In December 2013, SIGN 136 was published, providing a comprehensive evidence-based guideline for the assessment and management of chronic pain in ScotlandAimsThis study aimed to examine the impact of SIGN 136 on opioid prescribing trends and costs across the whole of Scotland.MethodsOpioid prescribing data and average cost per item were obtained from Public Health Scotland. An interrupted time series analysis examined the effects of SIGN 136 publication on the number of items prescribed per 1,000 population per quarter for 29 opioids (or opioid-containing combinations) from 2005 to 2019 inclusive. Exploratory analysis was conducted in NHS Tayside and NHS Fife combined and then up-scaled to all 14 NHS Scotland health boards. A similar approach was also used to assess the effect of SIGN 136 on estimated gross ingredient costs per quarter.ResultsAt six years post-intervention there was a relative reduction in opioid prescribing of 18.8% (95% CI: 16.0-21.7) across Scotland. There was also a relative reduction of 22.8% (95%: 14.9-30.1) in gross ingredient cost nationally. Opioid prescribing increased significantly pre-intervention across all 14 NHS Scotland health boards (2.19 items per 1000 population per quarter), followed by a non-significant change in level and a significant negative change in trend post-intervention (−2.69 items per 1000 population per quarter). Similar findings were observed locally in NHS Tayside and NHS Fife.ConclusionsThe publication of SIGN 136 coincided with a statistically significant reduction in opioid prescribing rates in Scotland and suggests that changes in clinical policy are having a positive effect on prescribing practices in primary care. These prescribing trends appear to be in contrast to the UK as a whole.

scholarly journals Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Andrea L. Schaffer ◽  
Timothy A. Dobbins ◽  
Sallie-Anne Pearson
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Moving Average ◽  
Interrupted Time Series ◽  
Health Interventions ◽  
Policy Intervention ◽  
Arima Models ◽  
Interrupted Time Series Analysis ◽  
Autoregressive Integrated Moving Average ◽  
The Impact

Abstract Background Interrupted time series analysis is increasingly used to evaluate the impact of large-scale health interventions. While segmented regression is a common approach, it is not always adequate, especially in the presence of seasonality and autocorrelation. An Autoregressive Integrated Moving Average (ARIMA) model is an alternative method that can accommodate these issues. Methods We describe the underlying theory behind ARIMA models and how they can be used to evaluate population-level interventions, such as the introduction of health policies. We discuss how to select the shape of the impact, the model selection process, transfer functions, checking model fit, and interpretation of findings. We also provide R and SAS code to replicate our results. Results We illustrate ARIMA modelling using the example of a policy intervention to reduce inappropriate prescribing. In January 2014, the Australian government eliminated prescription refills for the 25 mg tablet strength of quetiapine, an antipsychotic, to deter its prescribing for non-approved indications. We examine the impact of this policy intervention on dispensing of quetiapine using dispensing claims data. Conclusions ARIMA modelling is a useful tool to evaluate the impact of large-scale interventions when other approaches are not suitable, as it can account for underlying trends, autocorrelation and seasonality and allows for flexible modelling of different types of impacts.

scholarly journals Massive social protests amid the pandemic in selected Colombian cities: Did they increase COVID-19 cases?

2021 ◽  
Author(s):  
Jose Moreno-Montoya ◽  
Laura A Rodriguez Villamizar ◽  
Alvaro Javier Idrovo
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Interrupted Time Series ◽  
Series Data ◽  
Arima Models ◽  
Interrupted Time Series Analysis ◽  
Social Protests ◽  
Show Evidence ◽  
The Impact

Background. Since April 28, 2021, in Colombia there are social protests with numerous demonstrations in various cities. This occurs whereas the country faces the third wave of the COVID-19 pandemic. The aim of this study was to assess the effect of social protests on the number and trend of the confirmed COVID-19 cases in some selected Colombian cities where social protests had more intensity. Methods. We performed and interrupted time-series analysis (ITSA) and Autoregressive Integrated Moving Average (ARIMA) models, based on the confirmed COVID-19 cases in Colombia, between March 1 and May 15, 2021, for the cities of Bogota, Cali, Barranquilla, Medellin, and Bucaramanga. The ITSA models estimated the impact of social demonstrations on the number and trend of cases for each city by using Newey-West standard errors and ARIMA models assessed the overall pattern of the series and effect of the intervention. We considered May 2, 2021, as the intervention date for the analysis, five days after social demonstrations started in the country. Findings. During the study period the number of cases by city was 1,014,815 for Bogota, 192,320 for Cali, 175,269 for Barranquilla, 311,904 for Medellin, and 62,512 for Bucaramanga. Heterogeneous results were found among cities. Only for the cities of Cali and Barranquilla statistically significant changes in trend of the number of cases were obtained after the intervention: positive in the first city, negative in the second one. None ARIMA models show evidence of abrupt changes in the trend of the series for any city and intervention effect was only positive for Bucaramanga. Interpretation. The findings confer solid evidence that social protests had an heterogenous effect on the number and trend of COVID-19 cases. Divergent effects might be related to the epidemiologic time of the pandemic and the characteristics of the social protests. Assessing the effect of social protests within a pandemic is complex and there are several methodological limitations. Further analyses are required with longer time-series data.

scholarly journals Does admission prevalence change after reconfiguration of inpatient services? An interrupted time series analysis of the impact of reconfiguration in five centres

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Joanne Martin ◽  
Edwin Amalraj Raja ◽  
Steve Turner
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Emergency Admission ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Emergency Admissions ◽  
Series Analysis ◽  
Inpatient Services ◽  
The Impact ◽  
Admission Prevalence

Abstract Background Service reconfiguration of inpatient services in a hospital includes complete and partial closure of all emergency inpatient facilities. The “natural experiment” of service reconfiguration may give insight into drivers for emergency admissions to hospital. This study addressed the question does the prevalence of emergency admission to hospital for children change after reconfiguration of inpatient services? Methods There were five service reconfigurations in Scottish hospitals between 2004 and 2018 where emergency admissions to one “reconfigured” hospital were halted (permanently or temporarily) and directed to a second “adjacent” hospital. The number of emergency admissions (standardised to /1000 children in the regional population) per month to the “reconfigured” and “adjacent” hospitals was obtained for five years prior to reconfiguration and up to five years afterwards. An interrupted time series analysis considered the association between reconfiguration and admissions across pairs comprised of “reconfigured” and “adjacent” hospitals, with adjustment for seasonality and an overall rising trend in admissions. Results Of the five episodes of reconfiguration, two were immediate closure, two involved closure only to overnight admissions and one with overnight closure for a period and then closure. In “reconfigured” hospitals there was an average fall of 117 admissions/month [95% CI 78, 156] in the year after reconfiguration compared to the year before, and in “adjacent” hospitals admissions rose by 82/month [32, 131]. Across paired reconfigured and adjacent hospitals, in the months post reconfiguration, the overall number of admissions to one hospital pair slowed, in another pair admissions accelerated, and admission prevalence was unchanged in three pairs. After reconfiguration in one hospital, there was a rise in admissions to a third hospital which was closer than the named “adjacent” hospital. Conclusions There are diverse outcomes for the number of emergency admissions post reconfiguration of inpatient facilities. Factors including resources placed in the community after local reconfiguration, distance to the “adjacent” hospital and local deprivation may be important drivers for admission pathways after reconfiguration. Policy makers considering reconfiguration might consider a number of factors which may be important determinants of admissions post reconfiguration.

Recession and risk of suicide in Denmark during the 2009 global financial crisis: an ecological register-based study

2021 ◽  
pp. 140349482110132
Author(s):  
Agnieszka Konieczna ◽  
Sarah Grube Jakobsen ◽  
Christina Petrea Larsen ◽  
Erik Christiansen
Keyword(s):  
Time Series ◽  
Financial Crisis ◽  
Time Series Analysis ◽  
Suicide Rate ◽  
Global Financial Crisis ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Prevention Programmes ◽  
The Impact

Aim: The aim of this study is to analyse the potential impact from the financial crisis (onset in 2009) on suicide rates in Denmark. The hypothesis is that the global financial crisis raised unemployment which leads to raising the suicide rate in Denmark and that the impact is most prominent in men. Method: This study used an ecological study design, including register data from 2001 until 2016 on unemployment, suicide, gender and calendar time which was analysed using Poisson regression models and interrupted time series analysis. Results: The correlation between unemployment and suicide rates was positive in the period and statistically significant for all, but at a moderate level. A dichotomised version of time (calendar year) showed a significant reduction in the suicide rate for women (incidence rate ratio 0.87, P=0.002). Interrupted time series analysis showed a significant decreasing trend for the overall suicide rate and for men in the pre-recession period, which in both cases stagnated after the onset of recession in 2009. The difference between the genders’ suicide rate changed significantly at the onset of recession, as the rate for men increased and the rate for women decreased. Discussion: The Danish social welfare model might have prevented social disintegration and suicide among unemployed, and suicide prevention programmes might have prevented deaths among unemployed and mentally ill individuals. Conclusions: We found some indications for gender-specific differences from the impact of the financial crises on the suicide rate. We recommend that men should be specifically targeted for appropriate prevention programmes during periods of economic downturn.

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